Resource allocation for tasks of unknown complexity

ABSTRACT

For a task that has been partially executed, a residual complexity index is computed, the task being of a complexity that cannot be ascertained prior to executing the task. An evaluation is made whether the residual complexity index exceeds a cost of a resource that should be considered for allocation to the task. When the evaluation is affirmative, a priority of the task is established relative to a second task. The resource is scheduled to perform the task according to a timing, the timing being determined using the cost of the resource. The resource is allocated to the task according to the timing.

TECHNICAL FIELD

The present invention relates generally to a method, system, andcomputer program product for assigning a variety of resources to avariety of tasks that have a high degree of uncertainty associated withthem. More particularly, the present invention relates to a method,system, and computer program product for resource allocation for a taskof unknown complexity.

BACKGROUND

Tasks take a variety of forms. In some cases, a task is a workload thathas to be processed on a computing platform. In some other cases, thetask is a job opportunity that has to be filled using a suitablecandidate.

In one case, the resource that is needed to complete the workload-typetask is a computing resource. In another case, the resource that isneeded to complete the recruitment-type task is a recruiter.

Regardless of the type, a task has to be planned in such a way that thetask has a desired level of likelihood of completion. Regardless of thetype, each resource has a cost associated with it, which adds to thecost of completing the task.

A complexity of a task is an indication of a level of difficulty incompleting the task or a phase in the task. In many cases, a complexityof a task is known at the planning stage and adequate resources can beassigned to the task to ensure that the task reaches completion. Forexample, in a workload-type task, a history of executing similarworkloads can be used to establish a complexity of the task at hand, andresources similar to those used in the historical execution of similarworkloads can be assigned to the task at hand. As another example,recruiting a CEO for a company has a known level of difficulty fromother companies' experience in recruiting CEOs, and therefore, asuitable recruiter can be engaged from the beginning of the search toafford the recruitment effort a desirable likelihood of success.

In many cases, the complexity of a task is unknown or undeterminable atthe planning stage. A determination about the suitable types ofresources to assign to the task, when to assign those resources to thetask, and whether those resources will be available at that time aredifficult to make. For example, in a workload-type task, a workload thathas highly variant characteristics from execution to execution, or aworkload that has not been processed before, or a workload whose outcomeand requirements are unknown, form some example of workloads with suchcomplexity. As an example, a workload that simulates the evolution ofgalaxies in a specific area of the universe can run for an amount oftime that is not known a prior, can consume resources that are notidentifiable a priori, or both. It is difficult to know at the planningstage of such a workload whether supercomputing resources should beassigned to the workload, when they should be assigned, and in whatpriority relative to other tasks demanding the supercomputing resources.

Similarly, a job specification that calls for a specific mix of skillsand experience in a job candidate can be of an unknown complexity. It isdifficult to determine whether assigning a recruiter to the recruitmenteffort is justified or needed for that specific mix, at what point intime would such assignment improve the likelihood of successfulrecruitment or be too late for the project that needs the skills, andwhat priority should the recruiter give to this job specification versusother job specifications that also demand the recruiter's attention.

SUMMARY

The illustrative embodiments provide a method, system, and computerprogram product. An embodiment includes a method that computes, using aprocessor and a memory, for a task that has been partially executed, aresidual complexity index, the task being of a complexity that cannot beascertained prior to executing the task. The embodiment evaluateswhether the residual complexity index exceeds a cost of a resource thatshould be considered for allocation to the task. The embodimentestablishes, responsive to the evaluating being affirmative, a priorityof the task relative to a second task. The embodiment schedules theresource to perform the task according to a timing, the timing beingdetermined using the cost of the resource. The embodiment allocates theresource to the task according to the timing.

An embodiment includes a computer usable program product. The computerusable program product includes one or more computer-readable storagedevices, and program instructions stored on at least one of the one ormore storage devices.

An embodiment includes a computer system. The computer system includesone or more processors, one or more computer-readable memories, and oneor more computer-readable storage devices, and program instructionsstored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofthe illustrative embodiments when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented;

FIG. 2 depicts a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIG. 3 depicts a block diagram of an example configuration for resourceallocation for a task of unknown complexity in accordance with anillustrative embodiment;

FIG. 4 depicts a graph of an example resource allocation in accordancewith an illustrative embodiment; and

FIG. 5 depicts a flowchart of an example process for resource allocationfor a task of unknown complexity in accordance with an illustrativeembodiment.

DETAILED DESCRIPTION

Hereinafter, a “task” refers to any type of task contemplated herein,including but not limited to tasks to be executed on a data processingsystem unless expressly distinguished where used. Hereinafter, a“resource” refers to any type of resource contemplated herein forcompletion of the corresponding type of task unless expresslydistinguished where used.

The illustrative embodiments recognize that for tasks that haveuncertain complexity or are generally highly uncertain tasks, resourceallocation for a successful completion is a difficult problem to solve.Particularly, the illustrative embodiments recognize that correctresource type identification is an important problem, and assigning anincorrect type of resource or insufficient resource to the task can leadto a failure in the performance of the task.

The illustrative embodiments recognize that even with the correct type,identifying the needed resource type and assigning a correspondingresource too early in the task execution can result in cost overrun. Theillustrative embodiments also recognize that similarly, identifying aneeded resource type and assigning a corresponding resource too late canresult in the task still failing despite the resource expenditure.

The illustrative embodiments recognize that even with the correct typeof resource identified and the correct timing of the resourceidentified, assigning a resource to the task without considering otherdemands on the resource at that time can cause another task toexperience a failure. The illustrative embodiments recognize that evenwith the consideration of the other demands, assigning the resourcewithout establishing the priority of the task for the resource relativeto the other demands can cause a suboptimal use of the resource andpotential failure of other tasks assigned to the resource.

The illustrative embodiments recognize that the presently availabletools or solutions do not address these needs or provide adequatesolutions for these needs. The illustrative embodiments used to describethe invention generally address and solve the above-described problemsand other problems related to resource allocation for a task of unknowncomplexity.

An embodiment can be implemented as a software application. Theapplication implementing an embodiment can be configured as amodification of an existing resource allocation system, as a separateapplication that operates in conjunction with an existing resourceallocation system, a standalone application, or some combinationthereof.

An embodiment analyzes a resource dynamic in a given environment. Forexample, in a data processing environment, for allocating resources to aworkload-type task, the embodiment determines a state of the variousresources—e.g., various data processing systems or parts thereof,various tasks using or demanding those resources, a degree of difficultyin obtaining or allocating those resources, and other factors that wouldhave an impact on the allocation of those resources to the task, ifneeded later on. As another example, in a recruitment-type task, theembodiment determines an availability fluctuation of a desired skill setin the job market, types of recruiters available and demands on them, adegree of difficulty in obtaining a candidate with a specified talent,and other factors that would have an impact on the recruitment effort.

An embodiment determines whether the resource dynamics justifyallocating a specific type of resource to the task at hand. For example,given the resource dynamics in a data processing environment, some tasksare likely to succeed when configured according to a default resourceconfiguration for workloads. In other cases, it may be apparent from thedemand and use pattern of resources in the data processing environmentthat a default configuration might be an under-allocation for theworkload.

Similarly, given the resource dynamics in a jobs marketplace, some jobsmay fill without needing a recruiter resource—such as by candidatesself-applying for those job openings. In other cases, it might beapparent from the small number of resumes having the skill set requiredin a job specification, that a recruiter with experience in thepertinent skills might be needed.

If a specific resource allocation is not justified, or insufficientinformation is available to justify the allocation, an embodimentconfigures the task with the default configuration of resources andinitializes the task with that configuration. If a specific resourceallocation is justified, an embodiment allocates the specific resourceto the task and initializes the task with that resource.

While the task is executing, i.e., being performed using the resourceslast allocated to the task, an embodiment computes a residual complexityof the task. A residual complexity of the task is a complexity of theremainder—or residual—of the task that has to be processed for asuccessful outcome.

To compute the residual complexity, an embodiment performs one or moreof the following operations—

Task state quantification—in this operation, the embodiment estimates atask complexity at a time. For example, in recruitment use-case, theestimated task complexity is a factor of a number of competing jobopenings existing in the jobs marketplace at a time.

Task state estimation—in this operation, the embodiment determines aprobability of completion of the task from the determined present state.For example, in the recruitment use-case, this probability would be theprobability of filling an open job requisition in one time period giventhe current estimated number of other similar jobs in the market, age ofrequisition etc.

Task state prediction (Markov Chain)—in this operation, the embodimentdetermines the probability with which the task complexity is expected toincrease or decrease in one time period. In the recruitment use-case,this is the probability of competing number of jobs increasing by Nunits in time period. Task state prediction is computed for severalvalues of N.

Given the computed residual complexity of the task in progress, anembodiment determines a configuration of a resource, e.g., a resourcetype that is identifiable at this point in the execution of the task andis predicted to be needed to achieve a successful completion from thispoint in the execution. Once the desired resource or resource type isidentified, the embodiment computes a cost associated with allocatingthe identified resource of the type to the task.

The cost of the resource can be determined in one or more of thefollowing ways—

Stochastic Optimization—this method identifies the maximal value ofallocating a resource to the task at any time given the estimatedcomplexity, predicted change in complexity and the expected rate ofcompletion of task. The maximal value changes over time as newobservations are made and complexity is better understood. This methodthus identifies the minimum resource cost at which the task has to beexecuted for timely completion when there are no competing tasks.

Monotone Search—this method identifies an optimal subset of tasks amongthe task set for which allocation of one resource each would result inmaximal gain in value. The tasks for which a resource is allocated wouldhave the computed maximal value at least greater than the cost of theresource otherwise it is loss making allocation.

An embodiment uses the stochastic optimization method alone. Anotherembodiment uses the monotone search method alone. Another embodimentalternates between the two methods in different iterations of the costcomputations.

The residual complexity can also be translated into a cost. In otherwords, a cost value can be derived from the residual complexity of atask-in-progress. Some examples of cost equivalency of the residualcomplexity include but are not limited to a cost or penalty associatedwith breaching a service level agreement, and a cost or time increase ina project if a job is not fulfilled. If the residual complexity or aderivative thereof exceeds the cost of the desired resource, theembodiment concludes that allocating the resource to the task isjustified.

Upon justification of the allocation, an embodiment further estimatesthe demand for the desired resource by tasks other than thetask-in-progress. The estimated demand is for a period extending fromthe present time up to either an estimated completion time of thepresent task-in-progress, or up to a time when the task-in-progress isexpected to reach a phase where the desired resource can be deallocatedfrom the task-in-progress.

Based on the estimated demand, and the expected or actual other taskscompeting for the resource, an embodiment computes a relative priorityof the task-in-progress. The relative priority of the task-in-progressis relative to the other expected or actual tasks that are competing forthe desired resource.

The embodiment allocates the desired resource to the task-in-progresswhile specifying the priority of processing the task-in-progress whileusing the resource. In other words, the resource is expected to only beavailable in an amount—measured by time for which the resource isavailable to the task or other suitable method of measuring theamount—that is proportional to the relative priority of the task.

From time to time, an embodiment recomputes the residual complexity ofthe task-in-progress, adjusts a type of resource, an allocation of theresource, a duration of the allocation, a relative priority of thetask-in-progress, or some combination thereof to continue to advance thetask towards a successful completion. In some cases, the residualcomplexity may not only increase but also decline as the taskprogresses. Accordingly, the embodiment may not only allocate resourcesbut also reduce an allocation or deallocate a resource as may bejustified by the residual complexity at the time.

The manner of resource allocation for a task of unknown complexitydescribed herein is unavailable in the presently available methods. Amethod of an embodiment described herein, when implemented to execute ona device or data processing system, comprises substantial advancement ofthe functionality of that device or data processing system in managingthe resource allocation to highly uncertain tasks or to tasks withuncertain complexity such that the tasks progress to completion withoutincurring unnecessary costs or adversely influencing other tasks.

The illustrative embodiments are described with respect to certain typesof tasks, resources, complexities, uncertainties, periods, costs,methods of computing costs, methods of computing a residual complexity,allocations, priorities, dynamics, devices, data processing systems,environments, components, and applications only as examples. Anyspecific manifestations of these and other similar artifacts are notintended to be limiting to the invention. Any suitable manifestation ofthese and other similar artifacts can be selected within the scope ofthe illustrative embodiments.

Furthermore, the illustrative embodiments may be implemented withrespect to any type of data, data source, or access to a data sourceover a data network. Any type of data storage device may provide thedata to an embodiment of the invention, either locally at a dataprocessing system or over a data network, within the scope of theinvention. Where an embodiment is described using a mobile device, anytype of data storage device suitable for use with the mobile device mayprovide the data to such embodiment, either locally at the mobile deviceor over a data network, within the scope of the illustrativeembodiments.

The illustrative embodiments are described using specific code, designs,architectures, protocols, layouts, schematics, and tools only asexamples and are not limiting to the illustrative embodiments.Furthermore, the illustrative embodiments are described in someinstances using particular software, tools, and data processingenvironments only as an example for the clarity of the description. Theillustrative embodiments may be used in conjunction with othercomparable or similarly purposed structures, systems, applications, orarchitectures. For example, other comparable mobile devices, structures,systems, applications, or architectures therefor, may be used inconjunction with such embodiment of the invention within the scope ofthe invention. An illustrative embodiment may be implemented inhardware, software, or a combination thereof.

The examples in this disclosure are used only for the clarity of thedescription and are not limiting to the illustrative embodiments.Additional data, operations, actions, tasks, activities, andmanipulations will be conceivable from this disclosure and the same arecontemplated within the scope of the illustrative embodiments.

Any advantages listed herein are only examples and are not intended tobe limiting to the illustrative embodiments. Additional or differentadvantages may be realized by specific illustrative embodiments.Furthermore, a particular illustrative embodiment may have some, all, ornone of the advantages listed above.

With reference to the figures and in particular with reference to FIGS.1 and 2, these figures are example diagrams of data processingenvironments in which illustrative embodiments may be implemented. FIGS.1 and 2 are only examples and are not intended to assert or imply anylimitation with regard to the environments in which differentembodiments may be implemented. A particular implementation may makemany modifications to the depicted environments based on the followingdescription.

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented. Data processingenvironment 100 is a network of computers in which the illustrativeembodiments may be implemented. Data processing environment 100 includesnetwork 102. Network 102 is the medium used to provide communicationslinks between various devices and computers connected together withindata processing environment 100. Network 102 may include connections,such as wire, wireless communication links, or fiber optic cables.

Clients or servers are only example roles of certain data processingsystems connected to network 102 and are not intended to exclude otherconfigurations or roles for these data processing systems. Server 104and server 106 couple to network 102 along with storage unit 108.Software applications may execute on any computer in data processingenvironment 100. Clients 110, 112, and 114 are also coupled to network102. A data processing system, such as server 104 or 106, or client 110,112, or 114 may contain data and may have software applications orsoftware tools executing thereon.

Only as an example, and without implying any limitation to sucharchitecture, FIG. 1 depicts certain components that are usable in anexample implementation of an embodiment. For example, servers 104 and106, and clients 110, 112, 114, are depicted as servers and clients onlyas example and not to imply a limitation to a client-serverarchitecture. As another example, an embodiment can be distributedacross several data processing systems and a data network as shown,whereas another embodiment can be implemented on a single dataprocessing system within the scope of the illustrative embodiments. Dataprocessing systems 104, 106, 110, 112, and 114 also represent examplenodes in a cluster, partitions, and other configurations suitable forimplementing an embodiment.

Device 132 is an example of a device described herein. For example,device 132 can take the form of a smartphone, a tablet computer, alaptop computer, client 110 in a stationary or a portable form, awearable computing device, or any other suitable device. Any softwareapplication described as executing in another data processing system inFIG. 1 can be configured to execute in device 132 in a similar manner.Any data or information stored or produced in another data processingsystem in FIG. 1 can be configured to be stored or produced in device132 in a similar manner.

Application 105 implements an embodiment described herein.

Servers 104 and 106, storage unit 108, and clients 110, 112, and 114 maycouple to network 102 using wired connections, wireless communicationprotocols, or other suitable data connectivity. Clients 110, 112, and114 may be, for example, personal computers or network computers.

In the depicted example, server 104 may provide data, such as bootfiles, operating system images, and applications to clients 110, 112,and 114. Clients 110, 112, and 114 may be clients to server 104 in thisexample. Clients 110, 112, 114, or some combination thereof, may includetheir own data, boot files, operating system images, and applications.Data processing environment 100 may include additional servers, clients,and other devices that are not shown.

In the depicted example, data processing environment 100 may be theInternet. Network 102 may represent a collection of networks andgateways that use the Transmission Control Protocol/Internet Protocol(TCP/IP) and other protocols to communicate with one another. At theheart of the Internet is a backbone of data communication links betweenmajor nodes or host computers, including thousands of commercial,governmental, educational, and other computer systems that route dataand messages. Of course, data processing environment 100 also may beimplemented as a number of different types of networks, such as forexample, an intranet, a local area network (LAN), or a wide area network(WAN). FIG. 1 is intended as an example, and not as an architecturallimitation for the different illustrative embodiments.

Among other uses, data processing environment 100 may be used forimplementing a client-server environment in which the illustrativeembodiments may be implemented. A client-server environment enablessoftware applications and data to be distributed across a network suchthat an application functions by using the interactivity between aclient data processing system and a server data processing system. Dataprocessing environment 100 may also employ a service orientedarchitecture where interoperable software components distributed acrossa network may be packaged together as coherent business applications.

With reference to FIG. 2, this figure depicts a block diagram of a dataprocessing system in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as servers104 and 106, or clients 110, 112, and 114 in FIG. 1, or another type ofdevice in which computer usable program code or instructionsimplementing the processes may be located for the illustrativeembodiments.

Data processing system 200 is also representative of a data processingsystem or a configuration therein, such as data processing system 132 inFIG. 1 in which computer usable program code or instructionsimplementing the processes of the illustrative embodiments may belocated. Data processing system 200 is described as a computer only asan example, without being limited thereto. Implementations in the formof other devices, such as device 132 in FIG. 1, may modify dataprocessing system 200, such as by adding a touch interface, and eveneliminate certain depicted components from data processing system 200without departing from the general description of the operations andfunctions of data processing system 200 described herein.

In the depicted example, data processing system 200 employs a hubarchitecture including North Bridge and memory controller hub (NB/MCH)202 and South Bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 arecoupled to North Bridge and memory controller hub (NB/MCH) 202.Processing unit 206 may contain one or more processors and may beimplemented using one or more heterogeneous processor systems.Processing unit 206 may be a multi-core processor. Graphics processor210 may be coupled to NB/MCH 202 through an accelerated graphics port(AGP) in certain implementations.

In the depicted example, local area network (LAN) adapter 212 is coupledto South Bridge and I/O controller hub (SB/ICH) 204. Audio adapter 216,keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224,universal serial bus (USB) and other ports 232, and PCI/PCIe devices 234are coupled to South Bridge and I/O controller hub 204 through bus 238.Hard disk drive (HDD) or solid-state drive (SSD) 226 and CD-ROM 230 arecoupled to South Bridge and I/O controller hub 204 through bus 240.PCI/PCIe devices 234 may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbinary input/output system (BIOS). Hard disk drive 226 and CD-ROM 230may use, for example, an integrated drive electronics (IDE), serialadvanced technology attachment (SATA) interface, or variants such asexternal-SATA (eSATA) and micro-SATA (mSATA). A super I/O (SIO) device236 may be coupled to South Bridge and I/O controller hub (SB/ICH) 204through bus 238.

Memories, such as main memory 208, ROM 224, or flash memory (not shown),are some examples of computer usable storage devices. Hard disk drive orsolid state drive 226, CD-ROM 230, and other similarly usable devicesare some examples of computer usable storage devices including acomputer usable storage medium.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within dataprocessing system 200 in FIG. 2. The operating system may be acommercially available operating system for any type of computingplatform, including but not limited to server systems, personalcomputers, and mobile devices. An object oriented or other type ofprogramming system may operate in conjunction with the operating systemand provide calls to the operating system from programs or applicationsexecuting on data processing system 200.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs, such as application 105 in FIG. 1,are located on storage devices, such as in the form of code 226A on harddisk drive 226, and may be loaded into at least one of one or morememories, such as main memory 208, for execution by processing unit 206.The processes of the illustrative embodiments may be performed byprocessing unit 206 using computer implemented instructions, which maybe located in a memory, such as, for example, main memory 208, read onlymemory 224, or in one or more peripheral devices.

Furthermore, in one case, code 226A may be downloaded over network 201Afrom remote system 201B, where similar code 201C is stored on a storagedevice 201D. in another case, code 226A may be downloaded over network201A to remote system 201B, where downloaded code 201C is stored on astorage device 201D.

The hardware in FIGS. 1-2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS.1-2. In addition, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system.

In some illustrative examples, data processing system 200 may be apersonal digital assistant (PDA), which is generally configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data. A bus system may comprise one or morebuses, such as a system bus, an I/O bus, and a PCI bus. Of course, thebus system may be implemented using any type of communications fabric orarchitecture that provides for a transfer of data between differentcomponents or devices attached to the fabric or architecture.

A communications unit may include one or more devices used to transmitand receive data, such as a modem or a network adapter. A memory may be,for example, main memory 208 or a cache, such as the cache found inNorth Bridge and memory controller hub 202. A processing unit mayinclude one or more processors or CPUs.

The depicted examples in FIGS. 1-2 and above-described examples are notmeant to imply architectural limitations. For example, data processingsystem 200 also may be a tablet computer, laptop computer, or telephonedevice in addition to taking the form of a mobile or wearable device.

Where a computer or data processing system is described as a virtualmachine, a virtual device, or a virtual component, the virtual machine,virtual device, or the virtual component operates in the manner of dataprocessing system 200 using virtualized manifestation of some or allcomponents depicted in data processing system 200. For example, in avirtual machine, virtual device, or virtual component, processing unit206 is manifested as a virtualized instance of all or some number ofhardware processing units 206 available in a host data processingsystem, main memory 208 is manifested as a virtualized instance of allor some portion of main memory 208 that may be available in the hostdata processing system, and disk 226 is manifested as a virtualizedinstance of all or some portion of disk 226 that may be available in thehost data processing system. The host data processing system in suchcases is represented by data processing system 200.

With reference to FIG. 3, this figure depicts a block diagram of anexample configuration for resource allocation for a task of unknowncomplexity in accordance with an illustrative embodiment. Application302 is an example of application 105 in FIG. 1.

Task specification 304 is an input to application 302. Taskspecification 304 may be a specification of a workload-type task or ajob-specification for a recruitment-type task as described herein.Environment data 306 is another input to application 302. Environmentdata 306 includes information about resource usage and/or availability,resource demand forecast, competing task predictions, and the like, forworkload-type tasks. Environment data 306 includes the availability ofcertain skills in certain locations, and other such data that areindicative of conditions in the job marketplace for recruitment-typetasks.

Component 308 models an environment's dynamics using environment data306 as described herein. Component 310 initiates the task specified byinput task specification 304, such as in a default configuration. In aworkload-type task, initiation in a default configuration may mean thatthe task according to task specification 304 is configured to executewith a predetermined set of resources. In a recruitment-type task,initiation in a default configuration may mean that job specification304 is made available for candidates to respond without the involvementof a recruiter, or a with a predetermined level of recruiterinvolvement.

At some point during the execution of the task after the taskinitiation, component 312 computes a residual complexity of the task, asdescribed herein. Using the insight gained into the task due to thepartial execution of the task, component 314 determines or forecasts atype of resource that will be needed to complete the task successfullywithin task specification 304. Optionally, the determined type ofresource can be produced as output 315 for use by another system.Component 314 estimates the cost of a resource of the type forcompleting the task, e.g., by using stochastic optimization, monotonesearch, or a combination thereof.

Component 316 estimates other demand on the resource. Using the costdetermined by component 316 and the demand estimated by component 316,component 318 prioritizes the remaining portion of the partiallyexecuted task relative to the other forecasted or actual demand for theresource. Optionally, the determined relative priority of the task canbe produced as output 317 for use by another system.

Component 318 schedules the resource according to the priority and costrelated timing consideration, as described herein. Optionally, thedetermined timing of resource allocation can be produced as output 319for use by another system. Component 320 allocates the resource, oradjusts the allocation of the resource to the task according to thescheduling produced by component 316.

With reference to FIG. 4, this figure depicts a graph of an exampleresource allocation in accordance with an illustrative embodiment.Application 302 can perform the resource allocation as depicted in graph400.

Graph 400 shows the allocation strategy used in application 302, whenseveral tasks are competing for a resource. Only as an overly simplifiedexample and without implying any limitations thereto on the illustrativeembodiments, consider that the resource is a single resource called“machine”, which has a cost of 2200 Dollars per unit of consumption. Thetasks competing for the machine are task labeled “J1011”, task labeled“J1013”, task labeled “J1014”, task labeled “J1017”, and task labeled“J1018”.

Graph 400 plots changes in residual complexities of tasks J1011-J1018,where the residual complexities have been converted to a cost index on ascale comparable to the cost of the machine. The competing tasks age, orprogress in their execution, from time T0 to T1 to T2 to T3 and so on.Graph 402 corresponds to the changes in the residual complexity index oftask J1011; graph 404 corresponds to the changes in the residualcomplexity index of task J1013; graph 406 corresponds to the changes inthe residual complexity index of task J1014; graph 408 corresponds tothe changes in the residual complexity index of task J1017; and graph410 corresponds to the changes in the residual complexity index of taskJ1018.

At approximately time T17, the residual complexity index of graph 410crosses or exceeds the cost of the machine. Accordingly, the machine isscheduled for allocation to task J1018 at or around time T17 dependingon the considerations described herein. The residual complexity of taskJ1017 never exceeds the cost of the resource, according to graph 408,and the machine is never allocated to task J1017. Accordingly, taskJ1017 executes in a default configuration. At approximately time T30,task J1017 completes successfully executing using the defaultconfiguration.

At approximately time T33, the residual complexity index of graph 404crosses or exceeds the cost of the machine. Accordingly, the machine isscheduled for allocation to task J1013 at or around time T33 dependingon the considerations described herein. Other tasks age as depicted bytheir graphs and either become justifiable for the allocation of themachine or continue in their previous configurations.

With reference to FIG. 5, this figure depicts a flowchart of an exampleprocess for resource allocation for a task of unknown complexity inaccordance with an illustrative embodiment. Process 500 can beimplemented in application 302 in FIG. 3.

The application analyzes the resource dynamics in a given environmentwhere a specific task has to be performed (block 502). The applicationdetermines whether the task and/or the resource dynamics in theenvironment justify allocating a specific resource to the task (block504). If the resource allocation is justified (“Yes” path of block 504),the application enables an allocation of the specific resource to thetask (block 506). Process 500 then proceeds to block 510.

If a specific resource allocation is not justified (“No” path of block504), the application initializes the task using a default configuration(block 508). The application allows partial execution of the task tooccur (block 510).

After a partial execution of the task, the application computes aresidual complexity index of the task (block 508). The applicationdetermines whether the task will complete within the task specificationby executing as presently configured (block 514). If the task hasalready completed (“Complete” path of block 514), the application endsprocess 500 thereafter.

If the task is likely to complete within the task specification byexecuting as presently configured (“Yes” path of block 514), theapplication allows the task to continue in the present configuration(block 526). The application thereafter returns to block 510.

If the task is unlikely complete within the task specification byexecuting as presently configured (“No” path of block 514), theapplication computes a new resource requirement for the task, i.e., atype of resource that is needed to increase the likelihood that the taskwill complete successfully within the specification (block 516).

The application determines whether the residual complexity index of thetask exceeds the resource cost estimate (block 518). If the residualcomplexity index of the task does not exceed the resource cost estimate(“No” path of block 518), the application proceeds to block 526 andallows the task to continue in the present configuration.

If the residual complexity index of the task exceeds the resource costestimate (“Yes” path of block 518), the application estimates otherdemands on the resource (block 520). The application prioritizes thetask with the other tasks that are expected to be competing for theresource when the resource is expected to be allocated to the task(block 522). The application allocates the resource to the taskaccording to an allocation schedule or timing (block 524).

The application then returns process 500 to block 510. When the taskcompletes, the application causes process 500 to exit through the“Complete” path of block 514 and end thereafter.

Thus, a computer implemented method, system or apparatus, and computerprogram product are provided in the illustrative embodiments forresource allocation for a task of unknown complexity and other relatedfeatures, functions, or operations. Where an embodiment or a portionthereof is described with respect to a type of device, the computerimplemented method, system or apparatus, the computer program product,or a portion thereof, are adapted or configured for use with a suitableand comparable manifestation of that type of device.

Where an embodiment is described as implemented in an application, thedelivery of the application in a Software as a Service (SaaS) model iscontemplated within the scope of the illustrative embodiments. In a SaaSmodel, the capability of the application implementing an embodiment isprovided to a user by executing the application in a cloudinfrastructure. The user can access the application using a variety ofclient devices through a thin client interface such as a web browser(e.g., web-based e-mail), or other light-weight client-applications. Theuser does not manage or control the underlying cloud infrastructureincluding the network, servers, operating systems, or the storage of thecloud infrastructure. In some cases, the user may not even manage orcontrol the capabilities of the SaaS application. In some other cases,the SaaS implementation of the application may permit a possibleexception of limited user-specific application configuration settings.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A method comprising: computing, using a processorand a memory, for a task that has been partially executed, a residualcomplexity index indicative of difficulty of a remainder of the task,the task being of a complexity that cannot be ascertained prior toexecuting the task; evaluating whether the residual complexity indexexceeds a cost of a resource that should be considered for allocation tothe task; computing the cost of the resource in a plurality ofiterations, different iterations using different methods to determine aperiod during which the cost of the resource for any task is below athreshold; scheduling the resource to perform the task according to atiming, the timing being determined using the cost of the resource; andallocating the resource to the task according to the timing.
 2. Themethod of claim 1, wherein the timing is further determined using apriority.
 3. The method of claim 1, wherein a second task is forecastedto have a demand for the resource during the period.
 4. The method ofclaim 1, further comprising: converting, as a part of the evaluating,the residual complexity index to a cost value, the cost valuecorresponding to the residual complexity index being on a scale of thecost of the resource.
 5. The method of claim 1, further comprising:computing the cost of the resource, wherein the computing uses astochastic optimization to determine a period during which the cost ofthe resource for any task is below the threshold.
 6. The method of claim1, further comprising: computing the cost of the resource, wherein thecomputing uses a monotone search to determine a period during which thecost of the resource for use with the task is below the threshold. 7.The method of claim 1, wherein the computing the cost of the resource inthe plurality of iterations further comprises: computing the cost of theresource in a first iteration, wherein the first iteration uses astochastic optimization to determine a period during which the cost ofthe resource for any task is below a first threshold; and computing thecost of the resource in a second iteration, wherein the second iterationuses a monotone search to determine a period during which the cost ofthe resource for use with the task is below a second threshold.
 8. Themethod of claim 1, further comprising: determining, responsive to thecomplexity exhibited by the partial execution of the task, that theresource should be considered for allocation to the task.
 9. A computerusable program product comprising one or more computer-readable storagemedium, and program instructions stored on at least one of the one ormore storage medium, the stored program instructions comprising: programinstructions to compute, using a processor and a memory, for a task thathas been partially executed, a residual complexity index indicative ofdifficulty of a remainder of the task, the task being of a complexitythat cannot be ascertained prior to executing the task; programinstructions to evaluate whether the residual complexity index exceeds acost of a resource that should be considered for allocation to the task;program instructions to compute the cost of the resource in a pluralityof iterations, different iterations using different methods to determinea period during which the cost of the resource for any task is below athreshold; program instructions to schedule the resource to perform thetask according to a timing, the timing being determined using the costof the resource; and program instructions to allocate the resource tothe task according to the timing.
 10. The computer usable programproduct of claim 9, wherein the timing is further determined using apriority.
 11. The computer usable program product of claim 9, wherein asecond task is forecasted to have a demand for the resource during theperiod.
 12. The computer usable program product of claim 9, furthercomprising: program instructions to convert, as a part of theevaluating, the residual complexity index to a cost value, the costvalue corresponding to the residual complexity index being on a scale ofthe cost of the resource.
 13. The computer usable program product ofclaim 9, further comprising: program instructions to compute the cost ofthe resource, wherein the computing uses a stochastic optimization todetermine a period during which the cost of the resource for any task isbelow the threshold.
 14. The computer usable program product of claim 9,further comprising: program instructions to compute the cost of theresource, wherein the computing uses a monotone search to determine aperiod during which the cost of the resource for use with the task isbelow the threshold.
 15. The computer usable program product of claim 9,wherein the program instructions to compute the cost of the resource inthe plurality of iterations further comprise: program instructions tocompute the cost of the resource in a first iteration, wherein the firstiteration uses a stochastic optimization to determine a period duringwhich the cost of the resource for any task is below a first threshold;and program instructions to compute the cost of the resource in a seconditeration, wherein the second iteration uses a monotone search todetermine a period during which the cost of the resource for use withthe task is below a second threshold.
 16. The computer usable programproduct of claim 9, further comprising: program instructions todetermine, responsive to the complexity exhibited by the partialexecution of the task, that the resource should be considered forallocation to the task.
 17. The computer usable program product of claim9, wherein the computer usable program product is stored in a computerreadable storage device in a data processing system, and wherein thecomputer usable program product is transferred over a network from aremote data processing system.
 18. The computer usable program productof claim 9, wherein the computer usable program product is stored in acomputer readable storage device in a server data processing system, andwherein the computer usable program product is downloaded over a networkto a remote data processing system for use in the computer readablestorage device associated with the remote data processing system.
 19. Acomputer system comprising one or more processors, one or morecomputer-readable memories, and one or more computer-readable storagemedium, and program instructions stored on at least one of the one ormore storage medium for execution by at least one of the one or moreprocessors via at least one of the one or more memories, the storedprogram instructions comprising: program instructions to compute for atask that has been partially executed, a residual complexity indexindicative of difficulty of a remainder of the task, the task being of acomplexity that cannot be ascertained prior to executing the task;program instructions to evaluate whether the residual complexity indexexceeds a cost of a resource that should be considered for allocation tothe task; program instructions to compute the cost of the resource in aplurality of iterations, different iterations using different methods todetermine a period during which the cost of the resource for any task isbelow a threshold; program instructions to schedule the resource toperform the task according to a timing, the timing being determinedusing the cost of the resource; and program instructions to allocate theresource to the task according to the timing.
 20. The computer system ofclaim 19, wherein the timing is further determined using a priority.